Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1000
Missing cells74
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.8 KiB
Average record size in memory100.1 B

Variable types

DateTime1
Numeric2
Text9
Categorical1

Alerts

salary has 68 (6.8%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
ip_address has unique values Unique

Reproduction

Analysis started2024-10-29 02:38:32.846633
Analysis finished2024-10-29 02:38:34.448124
Duration1.6 second
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Distinct995
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2016-02-03 00:01:00
Maximum2016-02-03 23:59:55
2024-10-29T08:08:34.591246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T08:08:34.906357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

id
Real number (ℝ)

Uniform  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-29T08:08:35.070788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2024-10-29T08:08:35.278612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
961 1
 
0.1%
962 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%
Distinct198
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:35.738508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.639
Min length0

Characters and Unicode

Total characters5639
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.3%

Sample

1st rowAmanda
2nd rowAlbert
3rd rowEvelyn
4th rowDenise
5th rowCarlos
ValueCountFrequency (%)
samuel 11
 
1.1%
peter 11
 
1.1%
mark 11
 
1.1%
cheryl 10
 
1.0%
stephen 10
 
1.0%
marie 10
 
1.0%
jack 10
 
1.0%
dennis 10
 
1.0%
norma 10
 
1.0%
alice 9
 
0.9%
Other values (187) 882
89.6%
2024-10-29T08:08:36.315786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 631
 
11.2%
e 610
 
10.8%
n 457
 
8.1%
r 436
 
7.7%
i 409
 
7.3%
l 319
 
5.7%
h 268
 
4.8%
o 262
 
4.6%
t 222
 
3.9%
y 194
 
3.4%
Other values (35) 1831
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 631
 
11.2%
e 610
 
10.8%
n 457
 
8.1%
r 436
 
7.7%
i 409
 
7.3%
l 319
 
5.7%
h 268
 
4.8%
o 262
 
4.6%
t 222
 
3.9%
y 194
 
3.4%
Other values (35) 1831
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 631
 
11.2%
e 610
 
10.8%
n 457
 
8.1%
r 436
 
7.7%
i 409
 
7.3%
l 319
 
5.7%
h 268
 
4.8%
o 262
 
4.6%
t 222
 
3.9%
y 194
 
3.4%
Other values (35) 1831
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 631
 
11.2%
e 610
 
10.8%
n 457
 
8.1%
r 436
 
7.7%
i 409
 
7.3%
l 319
 
5.7%
h 268
 
4.8%
o 262
 
4.6%
t 222
 
3.9%
y 194
 
3.4%
Other values (35) 1831
32.5%
Distinct247
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:36.782843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.093
Min length3

Characters and Unicode

Total characters6093
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.3%

Sample

1st rowJordan
2nd rowFreeman
3rd rowMorgan
4th rowRiley
5th rowBurns
ValueCountFrequency (%)
barnes 10
 
1.0%
willis 9
 
0.9%
shaw 9
 
0.9%
patterson 9
 
0.9%
lane 8
 
0.8%
henderson 8
 
0.8%
gonzalez 8
 
0.8%
dixon 8
 
0.8%
jackson 8
 
0.8%
medina 8
 
0.8%
Other values (237) 915
91.5%
2024-10-29T08:08:37.432768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 663
 
10.9%
r 559
 
9.2%
n 521
 
8.6%
a 451
 
7.4%
o 426
 
7.0%
l 391
 
6.4%
s 388
 
6.4%
i 323
 
5.3%
t 209
 
3.4%
d 165
 
2.7%
Other values (36) 1997
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 663
 
10.9%
r 559
 
9.2%
n 521
 
8.6%
a 451
 
7.4%
o 426
 
7.0%
l 391
 
6.4%
s 388
 
6.4%
i 323
 
5.3%
t 209
 
3.4%
d 165
 
2.7%
Other values (36) 1997
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 663
 
10.9%
r 559
 
9.2%
n 521
 
8.6%
a 451
 
7.4%
o 426
 
7.0%
l 391
 
6.4%
s 388
 
6.4%
i 323
 
5.3%
t 209
 
3.4%
d 165
 
2.7%
Other values (36) 1997
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 663
 
10.9%
r 559
 
9.2%
n 521
 
8.6%
a 451
 
7.4%
o 426
 
7.0%
l 391
 
6.4%
s 388
 
6.4%
i 323
 
5.3%
t 209
 
3.4%
d 165
 
2.7%
Other values (36) 1997
32.8%

email
Text

Distinct985
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:37.758383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length20.632
Min length0

Characters and Unicode

Total characters20632
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique984 ?
Unique (%)98.4%

Sample

1st rowajordan0@com.com
2nd rowafreeman1@is.gd
3rd rowemorgan2@altervista.org
4th rowdriley3@gmpg.org
5th rowcburns4@miitbeian.gov.cn
ValueCountFrequency (%)
jberryc@usatoday.com 1
 
0.1%
dhudsone@blogger.com 1
 
0.1%
bwillisf@bluehost.com 1
 
0.1%
eandrewsg@cornell.edu 1
 
0.1%
swallaceh@netvibes.com 1
 
0.1%
clawsoni@vkontakte.ru 1
 
0.1%
rbellj@bandcamp.com 1
 
0.1%
dstevensk@cnet.com 1
 
0.1%
lramosl@sourceforge.net 1
 
0.1%
gbarnesm@google.ru 1
 
0.1%
Other values (974) 974
99.0%
2024-10-29T08:08:38.231257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1808
 
8.8%
e 1544
 
7.5%
c 1289
 
6.2%
a 1233
 
6.0%
r 1188
 
5.8%
m 1173
 
5.7%
n 1094
 
5.3%
. 1071
 
5.2%
s 1005
 
4.9%
@ 984
 
4.8%
Other values (29) 8243
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1808
 
8.8%
e 1544
 
7.5%
c 1289
 
6.2%
a 1233
 
6.0%
r 1188
 
5.8%
m 1173
 
5.7%
n 1094
 
5.3%
. 1071
 
5.2%
s 1005
 
4.9%
@ 984
 
4.8%
Other values (29) 8243
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1808
 
8.8%
e 1544
 
7.5%
c 1289
 
6.2%
a 1233
 
6.0%
r 1188
 
5.8%
m 1173
 
5.7%
n 1094
 
5.3%
. 1071
 
5.2%
s 1005
 
4.9%
@ 984
 
4.8%
Other values (29) 8243
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1808
 
8.8%
e 1544
 
7.5%
c 1289
 
6.2%
a 1233
 
6.0%
r 1188
 
5.8%
m 1173
 
5.7%
n 1094
 
5.3%
. 1071
 
5.2%
s 1005
 
4.9%
@ 984
 
4.8%
Other values (29) 8243
40.0%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Female
482 
Male
451 
67 

Length

Max length6
Median length4
Mean length4.696
Min length0

Characters and Unicode

Total characters4696
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th row

Common Values

ValueCountFrequency (%)
Female 482
48.2%
Male 451
45.1%
67
 
6.7%

Length

2024-10-29T08:08:38.421598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T08:08:38.556587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 482
51.7%
male 451
48.3%

Most occurring characters

ValueCountFrequency (%)
e 1415
30.1%
a 933
19.9%
l 933
19.9%
F 482
 
10.3%
m 482
 
10.3%
M 451
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1415
30.1%
a 933
19.9%
l 933
19.9%
F 482
 
10.3%
m 482
 
10.3%
M 451
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1415
30.1%
a 933
19.9%
l 933
19.9%
F 482
 
10.3%
m 482
 
10.3%
M 451
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1415
30.1%
a 933
19.9%
l 933
19.9%
F 482
 
10.3%
m 482
 
10.3%
M 451
 
9.6%

ip_address
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:38.900332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.289
Min length8

Characters and Unicode

Total characters13289
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row1.197.201.2
2nd row218.111.175.34
3rd row7.161.136.94
4th row140.35.109.83
5th row169.113.235.40
ValueCountFrequency (%)
239.182.219.189 1
 
0.1%
217.1.147.132 1
 
0.1%
1.197.201.2 1
 
0.1%
218.111.175.34 1
 
0.1%
7.161.136.94 1
 
0.1%
140.35.109.83 1
 
0.1%
169.113.235.40 1
 
0.1%
195.131.81.179 1
 
0.1%
143.232.204.137 1
 
0.1%
99.102.42.63 1
 
0.1%
Other values (990) 990
99.0%
2024-10-29T08:08:39.501749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.6%
1 2504
18.8%
2 1702
12.8%
4 913
 
6.9%
3 871
 
6.6%
5 852
 
6.4%
0 703
 
5.3%
9 699
 
5.3%
8 690
 
5.2%
7 687
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13289
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2504
18.8%
2 1702
12.8%
4 913
 
6.9%
3 871
 
6.6%
5 852
 
6.4%
0 703
 
5.3%
9 699
 
5.3%
8 690
 
5.2%
7 687
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13289
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2504
18.8%
2 1702
12.8%
4 913
 
6.9%
3 871
 
6.6%
5 852
 
6.4%
0 703
 
5.3%
9 699
 
5.3%
8 690
 
5.2%
7 687
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13289
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.6%
1 2504
18.8%
2 1702
12.8%
4 913
 
6.9%
3 871
 
6.6%
5 852
 
6.4%
0 703
 
5.3%
9 699
 
5.3%
8 690
 
5.2%
7 687
 
5.2%

cc
Text

Distinct710
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:39.802511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length11.409
Min length0

Characters and Unicode

Total characters11409
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique709 ?
Unique (%)70.9%

Sample

1st row6759521864920116
2nd row
3rd row6767119071901597
4th row3576031598965625
5th row5602256255204850
ValueCountFrequency (%)
3583136326049310 1
 
0.1%
30475362189761 1
 
0.1%
5100143489438123 1
 
0.1%
3582151311470687 1
 
0.1%
3573030625927601 1
 
0.1%
3548019978887852 1
 
0.1%
375696484097714 1
 
0.1%
3554329250601579 1
 
0.1%
5020868434918236 1
 
0.1%
30392894850853 1
 
0.1%
Other values (699) 699
98.6%
2024-10-29T08:08:40.248283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1444
12.7%
3 1416
12.4%
6 1155
10.1%
0 1134
9.9%
4 1107
9.7%
2 1077
9.4%
7 1074
9.4%
1 1051
9.2%
8 994
8.7%
9 957
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1444
12.7%
3 1416
12.4%
6 1155
10.1%
0 1134
9.9%
4 1107
9.7%
2 1077
9.4%
7 1074
9.4%
1 1051
9.2%
8 994
8.7%
9 957
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1444
12.7%
3 1416
12.4%
6 1155
10.1%
0 1134
9.9%
4 1107
9.7%
2 1077
9.4%
7 1074
9.4%
1 1051
9.2%
8 994
8.7%
9 957
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1444
12.7%
3 1416
12.4%
6 1155
10.1%
0 1134
9.9%
4 1107
9.7%
2 1077
9.4%
7 1074
9.4%
1 1051
9.2%
8 994
8.7%
9 957
8.4%
Distinct120
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:40.553667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length7.507
Min length4

Characters and Unicode

Total characters7507
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)3.7%

Sample

1st rowIndonesia
2nd rowCanada
3rd rowRussia
4th rowChina
5th rowSouth Africa
ValueCountFrequency (%)
china 189
 
17.2%
indonesia 97
 
8.8%
russia 62
 
5.6%
philippines 45
 
4.1%
brazil 38
 
3.5%
portugal 38
 
3.5%
france 37
 
3.4%
poland 35
 
3.2%
sweden 25
 
2.3%
japan 20
 
1.8%
Other values (131) 515
46.8%
2024-10-29T08:08:41.047733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1076
14.3%
i 851
 
11.3%
n 836
 
11.1%
e 536
 
7.1%
s 361
 
4.8%
o 342
 
4.6%
r 309
 
4.1%
h 297
 
4.0%
l 295
 
3.9%
d 262
 
3.5%
Other values (41) 2342
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1076
14.3%
i 851
 
11.3%
n 836
 
11.1%
e 536
 
7.1%
s 361
 
4.8%
o 342
 
4.6%
r 309
 
4.1%
h 297
 
4.0%
l 295
 
3.9%
d 262
 
3.5%
Other values (41) 2342
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1076
14.3%
i 851
 
11.3%
n 836
 
11.1%
e 536
 
7.1%
s 361
 
4.8%
o 342
 
4.6%
r 309
 
4.1%
h 297
 
4.0%
l 295
 
3.9%
d 262
 
3.5%
Other values (41) 2342
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1076
14.3%
i 851
 
11.3%
n 836
 
11.1%
e 536
 
7.1%
s 361
 
4.8%
o 342
 
4.6%
r 309
 
4.1%
h 297
 
4.0%
l 295
 
3.9%
d 262
 
3.5%
Other values (41) 2342
31.2%
Distinct788
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:41.474845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length10
Mean length7.222
Min length0

Characters and Unicode

Total characters7222
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique771 ?
Unique (%)77.1%

Sample

1st row3/8/1971
2nd row1/16/1968
3rd row2/1/1960
4th row4/8/1997
5th row
ValueCountFrequency (%)
12/12/1983 2
 
0.2%
10/7/1986 2
 
0.2%
11/18/1958 2
 
0.2%
4/10/1965 2
 
0.2%
7/21/1986 2
 
0.2%
10/5/1982 2
 
0.2%
11/25/1998 2
 
0.2%
3/7/2000 2
 
0.2%
12/26/1966 2
 
0.2%
12/11/2000 2
 
0.2%
Other values (781) 787
97.5%
2024-10-29T08:08:42.023064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1604
22.2%
1 1573
21.8%
9 1170
16.2%
2 559
 
7.7%
8 426
 
5.9%
6 419
 
5.8%
7 381
 
5.3%
5 313
 
4.3%
0 280
 
3.9%
3 253
 
3.5%
Other values (13) 244
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 1604
22.2%
1 1573
21.8%
9 1170
16.2%
2 559
 
7.7%
8 426
 
5.9%
6 419
 
5.8%
7 381
 
5.3%
5 313
 
4.3%
0 280
 
3.9%
3 253
 
3.5%
Other values (13) 244
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 1604
22.2%
1 1573
21.8%
9 1170
16.2%
2 559
 
7.7%
8 426
 
5.9%
6 419
 
5.8%
7 381
 
5.3%
5 313
 
4.3%
0 280
 
3.9%
3 253
 
3.5%
Other values (13) 244
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 1604
22.2%
1 1573
21.8%
9 1170
16.2%
2 559
 
7.7%
8 426
 
5.9%
6 419
 
5.8%
7 381
 
5.3%
5 313
 
4.3%
0 280
 
3.9%
3 253
 
3.5%
Other values (13) 244
 
3.4%

salary
Real number (ℝ)

Missing 

Distinct932
Distinct (%)100.0%
Missing68
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean149005.36
Minimum12380.49
Maximum286592.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:42.210584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12380.49
5-th percentile26500.177
Q181559.717
median147274.52
Q3220103.46
95-th percentile274961.32
Maximum286592.99
Range274212.5
Interquartile range (IQR)138543.75

Descriptive statistics

Standard deviation79785.177
Coefficient of variation (CV)0.53545173
Kurtosis-1.2105125
Mean149005.36
Median Absolute Deviation (MAD)68183.345
Skewness0.039327608
Sum1.3887299 × 108
Variance6.3656744 × 109
MonotonicityNot monotonic
2024-10-29T08:08:42.401205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38292.37 1
 
0.1%
57194.86 1
 
0.1%
123903.42 1
 
0.1%
230451.15 1
 
0.1%
69227.11 1
 
0.1%
14247.62 1
 
0.1%
186469.43 1
 
0.1%
231067.84 1
 
0.1%
27234.28 1
 
0.1%
210001.95 1
 
0.1%
Other values (922) 922
92.2%
(Missing) 68
 
6.8%
ValueCountFrequency (%)
12380.49 1
0.1%
12834.8 1
0.1%
13268.99 1
0.1%
13375.17 1
0.1%
13574.77 1
0.1%
13673.35 1
0.1%
14247.62 1
0.1%
14673.04 1
0.1%
15270.86 1
0.1%
15423.09 1
0.1%
ValueCountFrequency (%)
286592.99 1
0.1%
286061.25 1
0.1%
285036.95 1
0.1%
284737.57 1
0.1%
284728.99 1
0.1%
284608.82 1
0.1%
284300.15 1
0.1%
284062.49 1
0.1%
283771.86 1
0.1%
283517.64 1
0.1%

title
Text

Distinct182
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-10-29T08:08:42.791393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length26
Mean length14.637
Min length0

Characters and Unicode

Total characters14637
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)3.4%

Sample

1st rowInternal Auditor
2nd rowAccountant IV
3rd rowStructural Engineer
4th rowSenior Cost Accountant
5th row
ValueCountFrequency (%)
engineer 120
 
6.4%
assistant 64
 
3.4%
analyst 59
 
3.1%
manager 58
 
3.1%
senior 51
 
2.7%
i 50
 
2.6%
ii 48
 
2.5%
iv 47
 
2.5%
iii 45
 
2.4%
accountant 40
 
2.1%
Other values (118) 1306
69.2%
2024-10-29T08:08:43.378107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1384
 
9.5%
t 1122
 
7.7%
n 1119
 
7.6%
a 1091
 
7.5%
1085
 
7.4%
i 1056
 
7.2%
r 937
 
6.4%
s 832
 
5.7%
c 686
 
4.7%
o 684
 
4.7%
Other values (43) 4641
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1384
 
9.5%
t 1122
 
7.7%
n 1119
 
7.6%
a 1091
 
7.5%
1085
 
7.4%
i 1056
 
7.2%
r 937
 
6.4%
s 832
 
5.7%
c 686
 
4.7%
o 684
 
4.7%
Other values (43) 4641
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1384
 
9.5%
t 1122
 
7.7%
n 1119
 
7.6%
a 1091
 
7.5%
1085
 
7.4%
i 1056
 
7.2%
r 937
 
6.4%
s 832
 
5.7%
c 686
 
4.7%
o 684
 
4.7%
Other values (43) 4641
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1384
 
9.5%
t 1122
 
7.7%
n 1119
 
7.6%
a 1091
 
7.5%
1085
 
7.4%
i 1056
 
7.2%
r 937
 
6.4%
s 832
 
5.7%
c 686
 
4.7%
o 684
 
4.7%
Other values (43) 4641
31.7%
Distinct84
Distinct (%)8.5%
Missing6
Missing (%)0.6%
Memory size7.9 KiB
2024-10-29T08:08:43.618167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length217
Median length0
Mean length4.1338028
Min length0

Characters and Unicode

Total characters4109
Distinct characters394
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)2.5%

Sample

1st row1E+02
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
39
 
8.7%
1.00 9
 
2.0%
touch 9
 
2.0%
1e+02 7
 
1.6%
1e2 7
 
1.6%
etc/hosts 5
 
1.1%
💔 5
 
1.1%
💓 5
 
1.1%
💗 5
 
1.1%
💖 5
 
1.1%
Other values (149) 354
78.7%
2024-10-29T08:08:44.113987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
274
 
6.7%
. 209
 
5.1%
/ 135
 
3.3%
9 98
 
2.4%
t 87
 
2.1%
s 86
 
2.1%
e 80
 
1.9%
o 67
 
1.6%
l 65
 
1.6%
h 56
 
1.4%
Other values (384) 2952
71.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
274
 
6.7%
. 209
 
5.1%
/ 135
 
3.3%
9 98
 
2.4%
t 87
 
2.1%
s 86
 
2.1%
e 80
 
1.9%
o 67
 
1.6%
l 65
 
1.6%
h 56
 
1.4%
Other values (384) 2952
71.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
274
 
6.7%
. 209
 
5.1%
/ 135
 
3.3%
9 98
 
2.4%
t 87
 
2.1%
s 86
 
2.1%
e 80
 
1.9%
o 67
 
1.6%
l 65
 
1.6%
h 56
 
1.4%
Other values (384) 2952
71.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
274
 
6.7%
. 209
 
5.1%
/ 135
 
3.3%
9 98
 
2.4%
t 87
 
2.1%
s 86
 
2.1%
e 80
 
1.9%
o 67
 
1.6%
l 65
 
1.6%
h 56
 
1.4%
Other values (384) 2952
71.8%

Interactions

2024-10-29T08:08:33.614197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T08:08:33.307229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T08:08:33.742273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T08:08:33.481642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-29T08:08:44.232334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
genderidsalary
gender1.0000.0380.098
id0.0381.0000.002
salary0.0980.0021.000

Missing values

2024-10-29T08:08:33.939328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-29T08:08:34.179078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-29T08:08:34.383551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

registration_dttmidfirst_namelast_nameemailgenderip_addresscccountrybirthdatesalarytitlecomments
02016-02-03 07:55:291AmandaJordanajordan0@com.comFemale1.197.201.26759521864920116Indonesia3/8/197149756.53Internal Auditor1E+02
12016-02-03 17:04:032AlbertFreemanafreeman1@is.gdMale218.111.175.34Canada1/16/1968150280.17Accountant IV
22016-02-03 01:09:313EvelynMorganemorgan2@altervista.orgFemale7.161.136.946767119071901597Russia2/1/1960144972.51Structural Engineer
32016-02-03 00:36:214DeniseRileydriley3@gmpg.orgFemale140.35.109.833576031598965625China4/8/199790263.05Senior Cost Accountant
42016-02-03 05:05:315CarlosBurnscburns4@miitbeian.gov.cn169.113.235.405602256255204850South AfricaNaN
52016-02-03 07:22:346KathrynWhitekwhite5@google.comFemale195.131.81.1793583136326049310Indonesia2/25/198369227.11Account Executive
62016-02-03 08:33:087SamuelHolmessholmes6@foxnews.comMale232.234.81.1973582641366974690Portugal12/18/198714247.62Senior Financial Analyst
72016-02-03 06:47:068HarryHowellhhowell7@eepurl.comMale91.235.51.73Bosnia and Herzegovina3/1/1962186469.43Web Developer IV
82016-02-03 03:52:539JoseFosterjfoster8@yelp.comMale132.31.53.61South Korea3/27/1992231067.84Software Test Engineer I1E+02
92016-02-03 18:29:4710EmilyStewartestewart9@opensource.orgFemale143.28.251.2453574254110301671Nigeria1/28/199727234.28Health Coach IV
registration_dttmidfirst_namelast_nameemailgenderip_addresscccountrybirthdatesalarytitlecomments
9902016-02-03 20:31:10991RussellHuntrhuntri@yahoo.co.jpMale216.75.221.1503528271233562473Canada6/8/1970124353.04GIS Technical Architect
9912016-02-03 08:35:53992MarthaHowardmhowardrj@cdc.govFemale158.184.80.143566576585892705Poland7/18/2000158522.84Paralegal
9922016-02-03 11:48:54993PatriciaHendersonphendersonrk@delicious.comFemale4.2.237.1154041374756559Indonesia2/16/1960166651.78Programmer Analyst IV
9932016-02-03 01:14:13994CarolWilliamscwilliamsrl@army.milFemale53.242.60.20France1/5/1988120933.54Recruiter
9942016-02-03 00:18:26995JoseMccoyjmccoyrm@elpais.comMale117.37.215.98560222933605513180Norway7/30/1987275898.37Graphic Designer
9952016-02-03 10:30:59996DennisHarrisdharrisrn@eepurl.comMale178.180.111.236374288806662929Greece7/8/1965263399.54Editor
9962016-02-03 17:16:53997GloriaHamiltonghamiltonro@rambler.ruFemale71.50.39.137China4/22/197583183.54VP Product Management
9972016-02-03 05:02:20998NancyMorrisnmorrisrp@ask.com6.188.121.2213553564071014997Sweden5/1/1979NaNJunior Executive
9982016-02-03 02:41:32999AnnieDanielsadanielsrq@squidoo.comFemale97.221.132.3530424803513734China10/9/199118433.85Editor
9992016-02-03 09:52:181000JulieMeyerjmeyerrr@flavors.meFemale217.1.147.132374288099198540China222561.13